Published on in Vol 9, No 1 (2007):

Term Identification Methods for Consumer Health Vocabulary Development

Term Identification Methods for Consumer Health Vocabulary Development

Term Identification Methods for Consumer Health Vocabulary Development

Journals

  1. Brennan P. The Role of the National Library of Medicine in Advancing the Science of Health Disparities. Medical Care 2019;57(Suppl 2):S104 View
  2. Doing-Harris K, Livnat Y, Meystre S. Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system. Journal of Biomedical Semantics 2015;6(1) View
  3. White R, Horvitz E. Cyberchondria. ACM Transactions on Information Systems 2009;27(4):1 View
  4. Pierce C, Bouri K, Pamer C, Proestel S, Rodriguez H, Van Le H, Freifeld C, Brownstein J, Walderhaug M, Edwards I, Dasgupta N. Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts. Drug Safety 2017;40(4):317 View
  5. Wu D, Xin C, Bindhu S, Xu C, Sachdeva J, Brown J, Jung H. Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study. JMIR Formative Research 2020;4(8):e18123 View
  6. He D, Wang Z, Thaker K, Zou N. Translation and Expansion: Enabling Laypeople Access to the COVID-19 Academic Collection. Data and Information Management 2020;4(3):177 View
  7. Zhang J, Wolfram D, Wang P, Hong Y, Gillis R. Visualization of health-subject analysis based on query term co-occurrences. Journal of the American Society for Information Science and Technology 2008;59(12):1933 View
  8. Inthiran A, Alhashmi S, Ahmed P. Medical Information Retrieval Strategies. International Journal of Healthcare Information Systems and Informatics 2012;7(1):31 View
  9. Li F, Jin Y, Liu W, Rawat B, Cai P, Yu H. Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study. JMIR Medical Informatics 2019;7(3):e14830 View
  10. Sanchez Bocanegra C, Sevillano Ramos J, Rizo C, Civit A, Fernandez-Luque L. HealthRecSys: A semantic content-based recommender system to complement health videos. BMC Medical Informatics and Decision Making 2017;17(1) View
  11. Tapi Nzali M, Aze J, Bringay S, Lavergne C, Mollevi C, Optiz T. Reconciliation of patient/doctor vocabulary in a structured resource. Health Informatics Journal 2019;25(4):1219 View
  12. Chen J, Jagannatha A, Fodeh S, Yu H. Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach. JMIR Medical Informatics 2017;5(4):e42 View
  13. Mozer R, Miratrix L, Kaufman A, Jason Anastasopoulos L. Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality. Political Analysis 2020;28(4):445 View
  14. Zheng J, Yu H. Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study. Journal of Medical Internet Research 2017;19(3):e59 View
  15. Doing-Harris K, Zeng-Treitler Q. Computer-Assisted Update of a Consumer Health Vocabulary Through Mining of Social Network Data. Journal of Medical Internet Research 2011;13(2):e37 View
  16. Hou L, Kang H, Liu Y, Li L, Li J. Mining and standardizing chinese consumer health terms. BMC Medical Informatics and Decision Making 2018;18(S5) View
  17. Keselman A, Smith C, Divita G, Kim H, Browne A, Leroy G, Zeng-Treitler Q. Consumer Health Concepts That Do Not Map to the UMLS: Where Do They Fit?. Journal of the American Medical Informatics Association 2008;15(4):496 View
  18. Qenam B, Kim T, Carroll M, Hogarth M. Text Simplification Using Consumer Health Vocabulary to Generate Patient-Centered Radiology Reporting: Translation and Evaluation. Journal of Medical Internet Research 2017;19(12):e417 View
  19. Fernandez-Luque L, Karlsen R, Bonander J. Review of Extracting Information From the Social Web for Health Personalization. Journal of Medical Internet Research 2011;13(1):e15 View
  20. Kim M, Jeong I. Extraction of Hypertension-related Consumer Vocabulary and Mediator Vocabulary and Selection of Recommended Vocabulary. Korean Journal of Health Education and Promotion 2014;31(5):13 View
  21. Lavertu A, Altman R. RedMed: Extending drug lexicons for social media applications. Journal of Biomedical Informatics 2019;99:103307 View
  22. MacLean D, Heer J. Identifying medical terms in patient-authored text: a crowdsourcing-based approach. Journal of the American Medical Informatics Association 2013;20(6):1120 View
  23. Haga S, Mills R, Pollak K, Rehder C, Buchanan A, Lipkus I, Crow J, Datto M. Developing patient-friendly genetic and genomic test reports: formats to promote patient engagement and understanding. Genome Medicine 2014;6(7) View
  24. Nishimoto N, Yokooka Y, Yagahara A, Uesugi M, Ogasawara K. Quantitative evaluation of expression difference in report assignments between nursing and radiologic technology departments. Radiological Physics and Technology 2011;4(1):29 View
  25. He Z, Chen Z, Oh S, Hou J, Bian J. Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach. Journal of Biomedical Informatics 2017;69:75 View
  26. Lu Y, Wu Y, Liu J, Li J, Zhang P. Understanding Health Care Social Media Use From Different Stakeholder Perspectives: A Content Analysis of an Online Health Community. Journal of Medical Internet Research 2017;19(4):e109 View
  27. Boden C. Overcoming the linguistic divide: a barrier to consumer health information. Journal of the Canadian Health Libraries Association / Journal de l'Association des bibliothèques de la santé du Canada 2014;30(3):75 View
  28. Gu G, Zhang X, Zhu X, Jian Z, Chen K, Wen D, Gao L, Zhang S, Wang F, Ma H, Lei J. Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach. JMIR Medical Informatics 2019;7(2):e12704 View
  29. Zraick R, Azios M, Handley M, Bellon-Harn M, Manchaiah V. Quality and readability of internet information about stuttering. Journal of Fluency Disorders 2021;67:105824 View
  30. Monselise M, Greenberg J, Liang O, Pascua S, Kim H, Kelly M, Boone J, Yang C. An Automatic Approach to Extending the Consumer Health Vocabulary. Journal of Data and Information Science 2020;0(0) View
  31. He X, Zhang R, Alpert J, Zhou S, Adam T, Raisa A, Peng Y, Zhang H, Guo Y, Bian J. When text simplification is not enough: could a graph-based visualization facilitate consumers’ comprehension of dietary supplement information?. JAMIA Open 2021;4(1) View

Books/Policy Documents

  1. Inthiran A, Alhashmi S, Ahmed P. Business Intelligence. View
  2. Jiang L, Yang C. Social Computing, Behavioral-Cultural Modeling, and Prediction. View
  3. Song S, Choi Y, Chun H, Jeong C, Choi S, Sung W. U- and E-Service, Science and Technology. View
  4. Denecke K, Soltani N. Where Humans Meet Machines. View
  5. Inthiran A, Alhashmi S, Ahmed P. Advancing Medical Practice through Technology. View
  6. He Z. Social Web and Health Research. View
  7. Santini M, Jönsson A, Strandqvist W, Cederblad G, Nyström M, Alirezaie M, Lind L, Blomqvist E, Lindén M, Kristoffersson A. Cyber-Physical Systems for Social Applications. View